filter_map: Applies spatial filtering (2D or 3D)Ā¶

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Contents

% This m-file has been automatically generated using qMRgenBatch(filter_map)
    % Command Line Interface (CLI) is well-suited for automatization
    % purposes and Octave.
    %
    % Please execute this m-file section by section to get familiar with batch
    % processing for filter_map on CLI.
    %
    % Demo files are downloaded into filter_map_data folder.
    %
    % Written by: Agah Karakuzu, 2017
    % =========================================================================
    

I- DESCRIPTION

qMRinfo('filter_map'); % Describe the model
    
  filter_map:   Applies spatial filtering (2D or 3D)

    Assumptions: If a 3D volume is provided and 2D filtering is requested, each slice will be processsed independently

    Inputs:
    Raw                Input data to be filtered
    (Mask)             Binary mask to exclude voxels from smoothing

    Outputs:
    Filtered           Filtered output map (see FilterClass.m for more info)

    Protocol:
    NONE

    Options:
    (inherited from FilterClass)

    Example of command line usage:

    For more examples: a href="matlab: qMRusage(filter_map);"qMRusage(filter_map)/a

    Author: Ilana Leppert Dec 2018

    References:
    Please cite the following if you use this module:
Karakuzu A., Boudreau M., Duval T.,Boshkovski T., Leppert I.R., Cabana J.F.,
Gagnon I., Beliveau P., Pike G.B., Cohen-Adad J., Stikov N. (2020), qMRLab:
Quantitative MRI analysis, under one umbrella doi: 10.21105/joss.02343

    Reference page in Doc Center
    doc filter_map


    

II- MODEL PARAMETERS

a- create object

Model = filter_map;
    

b- modify options

         |- This section will pop-up the options GUI. Close window to continue.
    |- Octave is not GUI compatible. Modify Model.options directly.
Model = Custom_OptionsGUI(Model); % You need to close GUI to move on.
    

III- FIT EXPERIMENTAL DATASET

a- load experimental data

         |- filter_map object needs 2 data input(s) to be assigned:
    |-   Raw
    |-   Mask
data = struct();
    % Raw.nii.gz contains [128  128   35] data.
    data.Raw=double(load_nii_data('filter_map_data/Raw.nii.gz'));
    % Mask.nii.gz contains [128  128   35] data.
    data.Mask=double(load_nii_data('filter_map_data/Mask.nii.gz'));
    

b- fit dataset

           |- This section will fit data.
FitResults = FitData(data,Model,0);
    
=============== qMRLab::Fit ======================
    Operation has been started: filter_map
    Elapsed time is 23.015083 seconds.
    Operation has been completed: filter_map
    ==================================================
    

c- show fitting results

         |- Output map will be displayed.
    |- If available, a graph will be displayed to show fitting in a voxel.
    |- To make documentation generation and our CI tests faster for this model,
    we used a subportion of the data (40X40X40) in our testing environment.
    |- Therefore, this example will use FitResults that comes with OSF data for display purposes.
    |- Users will get the whole dataset (384X336X224) and the script that uses it for demo
    via qMRgenBatch(qsm_sb) command.
FitResults_old = load('FitResults/FitResults.mat');
    qMRshowOutput(FitResults_old,data,Model);
    

d- Save results

         |-  qMR maps are saved in NIFTI and in a structure FitResults.mat
    that can be loaded in qMRLab graphical user interface
    |-  Model object stores all the options and protocol.
    It can be easily shared with collaborators to fit their
    own data or can be used for simulation.
FitResultsSave_nii(FitResults, 'filter_map_data/Raw.nii.gz');
    Model.saveObj('filter_map_Demo.qmrlab.mat');
    
Warning: Directory already exists.
    

V- SIMULATIONS

   |- This section can be executed to run simulations for filter_map.

a- Single Voxel Curve

         |- Simulates Single Voxel curves:
    (1) use equation to generate synthetic MRI data
    (2) add rician noise
    (3) fit and plot curve
% Not available for the current model.
    

b- Sensitivity Analysis

         |-    Simulates sensitivity to fitted parameters:
    (1) vary fitting parameters from lower (lb) to upper (ub) bound.
    (2) run Sim_Single_Voxel_Curve Nofruns times
    (3) Compute mean and std across runs
% Not available for the current model.